Evidence map›Paper›PMID 34720896›Full record

ArticleFrontiers in systems neuroscience2021

Predicting Space Radiation Single Ion Exposure in Rodents: A Machine Learning Approach.

Matthew T Prelich, Mona Matar, Suleyman A Gokoglu, Christopher A Gallo, Alexander Schepelmann, Asad K Iqbal, Beth E Lewandowski, Richard A Britten, R K Prabhu, Jerry G Myers

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in systems neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.6field-weighted citation impact, top 30% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 4 institutions in 1 country.

Matthew T PrelichNASA Glenn Research Center, Cleveland, OH, United States.
Mona MatarNASA Glenn Research Center, Cleveland, OH, United States.
Suleyman A GokogluNASA Glenn Research Center, Cleveland, OH, United States.
Christopher A GalloNASA Glenn Research Center, Cleveland, OH, United States.
Alexander SchepelmannZIN Technologies, Inc., Cleveland, OH, United States.
Asad K IqbalZIN Technologies, Inc., Cleveland, OH, United States.
Beth E LewandowskiNASA Glenn Research Center, Cleveland, OH, United States.
Richard A BrittenDepartment of Radiation Oncology, Eastern Virginia Medical School, Norfolk, VA, United States.
R K PrabhuUniversities Space Research Association, Cleveland, OH, United States.
Jerry G MyersNASA Glenn Research Center, Cleveland, OH, United States.
Glenn Research Center · USZIN Technologies (United States) · USEastern Virginia Medical School · USUniversities Space Research Association · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study presents a data-driven machine learning approach to predict individual Galactic Cosmic Radiation (GCR) ion exposure for

Indexed as

cognitive impairmentGaussian naive Bayesimbalanced datasetsmachine learningradiation researchrodent studiesspace radiationsupport vector machine

Identifiers

PMID34720896
PMCPMC8555470
OpenAlexW3205060982

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.